Defining bounding surfaces within and between eolian and non-eolian deposits, Lower Jurassic Navajo Sandstone, Moab Area, Utah, U.S.A.: Implications for subdividing erg system strata

2021 ◽  
Vol 91 (12) ◽  
pp. 1275-1304
Author(s):  
Stephen T. Hasiotis ◽  
Marjorie A. Chan ◽  
Judith Totman Parrish

ABSTRACT A model-independent, sequence stratigraphic approach is used to define bounding surfaces in the Navajo Sandstone in order to identify an architectural hierarchy of genetically related sedimentary packages and the surfaces that bound them across multiple scales of both eolian and non-eolian components of an erg system. Seven bounding surfaces and eight depositional units are defined, from small to large scale. A lamina-deviation surface bounds wedge- and tabular-shaped sets of laminae and/or laminasets, separating those that have different angle orientations on the dune slipface. A bed-deviation surface bounds a succession of beds (crossbeds) that lie at different angles or orientations to bedding above, below, or adjacent to it. A bedset-deviation surface is curved, inclined, and/or wavy and irregular that bounds bedsets and their internal stratification patterns; that is, bed-deviation surfaces, and lamina-deviation surfaces. A simple surface is gently inclined with or without small, concave or convex segments that bound beds and bedsets. A composite surface is horizontal with or without concave, curved, or irregular portions of that surface. A complex surface is laterally extensive (∼ 1–10+ km) that regionally bounds and truncates underlying conterminous and interfingered eolian and non-eolian strata. An amalgamated surface is a regionally extensive (∼ 10 to 100s km) mappable unconformity, merged unconformities, and their laterally equivalent conformable surface that can exhibit local to regional pedogenic modification, lags, and significant (meters to 10s m) paleotopographic relief. The genetically related sedimentary packages typically bounded by like or higher-rank surfaces are defined as laminae, laminasets, bed, bedsets, and simple, composite, complex, and amalgamated units. Field relationships of strata and surfaces are key to reconstructing the interactions between eolian and non-eolian deposits and the processes they represent at the local, regional, and basin scale. This classification scheme can be applied to erg-system strata to fully integrate changes in diverse facies within and between contiguous deposits.

2020 ◽  
Author(s):  
Mariaines Di Dato ◽  
Rohini Kumar ◽  
Estanislao Pujades ◽  
Timo Houben ◽  
Sabine Attinger

<p>River stream is the result of several complex processes operating at basin scale. Therefore, the river catchment can be conceptualized as a series of interlinked compartments, which are characterized by their own response time to a rainfall event. Each compartment generates a flow component, such as the direct runoff, the interflow and the baseflow. The latter, typically generating from groundwater, is the slower portion of stream flow and plays a key role in studying the hydrological droughts.</p><p>In many catchment or large-scale hydrologic models, the groundwater dynamics are typically described by a linear reservoir model, which depends on the state of the reservoir and the parameter, known as recession coefficient or characteristic time. The characteristic time can be considered as the time needed until an aquifer reacts to a certain perturbation. So far, the characteristic time has been estimated by analyzing the slope of the recession (discharge) curve. However, as this method assumes that the recharge is zero within the basin, it may lead to inaccurate estimate when such a hypothesis is not fulfilled in reality.</p><p>The present work proposes to infer the characteristic time by using a stochastic approach based on spectral analysis. The catchment aquifer can be viewed as a filter, which modifies an input signal (e.g., rainfall or recharge) into an output signal (e.g., the baseflow or the hydraulic head). Since the transfer function, namely the ratio between the spectrum of baseflow and the spectrum of recharge, is dependent on the aquifer characteristics, it can be used to infer the aquifer parameters. In particular, the characteristic time is evaluated by fitting the spectrum and the variance of the measured baseflow with the analytical stochastic solutions for the linear reservoir. We compare six different methods for hydrograph separation, thereby highlighting a systematic uncertainty in determining the characteristic time due to the choice of filter used. To reduce the uncertainty in fitting, we will use the mesoscale Hydrological Model (mHM) (Samaniego et al., 2010; Kumar et al., 2013) to generate realistic time series for recharge. We apply the spectral analysis method to several river basins in Germany, with the goal to define a regionalization rule for characteristic time.</p><p> </p><p>References:</p><ul><li>Samaniego L., R. Kumar, S. Attinger (2010): Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46, W05523, doi:10.1029/2008WR007327.</li> <li>Kumar, R., L. Samaniego, and S. Attinger (2013): Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, doi:10.1029/2012WR012195</li> </ul>


2020 ◽  
Vol 90 (9) ◽  
pp. 1068-1093
Author(s):  
Marjorie A. Chan ◽  
Stephen T. Hasiotis ◽  
Judith Totman Parrish

ABSTRACT Extensive soft-sediment deformation (SSD) of multiple expressions and scales record active and dynamic events and processes in erg deposits of the Lower Jurassic Navajo Sandstone near Moab, Utah. The erg deposits preserve depositional environments of eolian dune, interdune, fluvial, playa, lake, and spring. A large range of SSD features, from intact beds showing little deformation to pervasively disturbed beds, exist in many of these deposits. A simplified classification index captures the different scales of SSD in ascending order of deformation intensity: 1) mostly intact bedding with small-scale wavy or undulatory deformation structures within single beds; 2) dish and flame structures; 3) meter-scale, kinked, slumped, rolled, overturned, vertical, and detached contorted crossbedding, and associated centimeter- to meter-scale pipes; and 4) disruptive diapirs and laterally extensive massive sandstone. The SSD features of deformed crossbed sets, diapirs, and massive sandstone beds, are consistently juxtaposed, and are thus genetically linked. Although the Navajo Sandstone has been considered a classic example of an extensive dry eolian system, both individual and combinations of strata bounded SSD features exemplify dynamic deformation, liquefaction, and fluidization that took place at various times after deposition. The lowest degree of deformation, SSD 1, is largely attributed to autogenic––inherent to the eolian system––or local allogenic processes. Larger degrees of deformation, SSD 2–4, were more likely produced by allogenic, external-forcing processes from regional changes in climate and/or near-surface groundwater conditions originating from the Uncompahgre uplift, with the deformation triggered by some event(s). Possible significant ground motion could have led to large-scale disruption in the Navajo sand sea across kilometer-scale intervals. The Navajo example establishes valuable hierarchical relationships of processes and products for recognizing and interpreting SSD in other ancient and modern eolian systems. This has particular relevance to sedimentary discoveries on Mars, where SSD features are visible from remote sensing imagery and rover exploration.


2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.


2021 ◽  
Vol 13 (15) ◽  
pp. 3023
Author(s):  
Jinghua Xiong ◽  
Shenglian Guo ◽  
Jiabo Yin ◽  
Lei Gu ◽  
Feng Xiong

Flooding is one of the most widespread and frequent weather-related hazards that has devastating impacts on the society and ecosystem. Monitoring flooding is a vital issue for water resources management, socioeconomic sustainable development, and maintaining life safety. By integrating multiple precipitation, evapotranspiration, and GRACE-Follow On (GRAFO) terrestrial water storage anomaly (TWSA) datasets, this study uses the water balance principle coupled with the CaMa-Flood hydrodynamic model to access the spatiotemporal discharge variations in the Yangtze River basin during the 2020 catastrophic flood. The results show that: (1) TWSA bias dominates the overall uncertainty in runoff at the basin scale, which is spatially governed by uncertainty in TWSA and precipitation; (2) spatially, a field significance at the 5% level is discovered for the correlations between GRAFO-based runoff and GLDAS results. The GRAFO-derived discharge series has a high correlation coefficient with either in situ observations and hydrological simulations for the Yangtze River basin, at the 0.01 significance level; (3) the GRAFO-derived discharge observes the flood peaks in July and August and the recession process in October 2020. Our developed approach provides an alternative way of monitoring large-scale extreme hydrological events with the latest GRAFO release and CaMa-Flood model.


Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 179
Author(s):  
Roxanne Ahmed ◽  
Terry Prowse ◽  
Yonas Dibike ◽  
Barrie Bonsal

Spring freshet is the dominant annual discharge event in all major Arctic draining rivers with large contributions to freshwater inflow to the Arctic Ocean. Research has shown that the total freshwater influx to the Arctic Ocean has been increasing, while at the same time, the rate of change in the Arctic climate is significantly higher than in other parts of the globe. This study assesses the large-scale atmospheric and surface climatic conditions affecting the magnitude, timing and regional variability of the spring freshets by analyzing historic daily discharges from sub-basins within the four largest Arctic-draining watersheds (Mackenzie, Ob, Lena and Yenisei). Results reveal that climatic variations closely match the observed regional trends of increasing cold-season flows and earlier freshets. Flow regulation appears to suppress the effects of climatic drivers on freshet volume but does not have a significant impact on peak freshet magnitude or timing measures. Spring freshet characteristics are also influenced by El Niño-Southern Oscillation, the Pacific Decadal Oscillation, the Arctic Oscillation and the North Atlantic Oscillation, particularly in their positive phases. The majority of significant relationships are found in unregulated stations. This study provides a key insight into the climatic drivers of observed trends in freshet characteristics, whilst clarifying the effects of regulation versus climate at the sub-basin scale.


2016 ◽  
Vol 02 (04) ◽  
pp. 1650023 ◽  
Author(s):  
Noémie Neverre ◽  
Patrice Dumas

This paper presents a methodology to project irrigation and domestic water demands on a regional to global scale, in terms of both quantity and economic value. Projections are distributed at the water basin scale. Irrigation water demand is projected under climate change. It is simply computed as the difference between crop potential evapotranspiration for the different stages of the growing season and available precipitation. Irrigation water economic value is based on a yield comparison approach between rainfed and irrigated crops using average yields. For the domestic sector, we project the combined effects of demographic growth, economic development and water cost evolution on future demands. The method consists in building three-part inverse demand functions in which volume limits of the blocks evolve with the level of GDP per capita. The value of water along the demand curve is determined from price-elasticity, price and demand data from the literature, using the point-expansion method, and from water cost data. This generic methodology can be easily applied to large-scale regions, in particular developing regions where reliable data are scarce. As an illustration, it is applied to Algeria, at the 2050 horizon, for demands associated to reservoirs. Our results show that domestic demand is projected to become a major water consumption sector. The methodology is meant to be integrated into large-scale hydroeconomic models, to determine inter-sectorial and inter-temporal water allocation based on economic valuation.


2021 ◽  
pp. 1-16
Author(s):  
Scott McKean ◽  
Simon Poirier ◽  
Henry Galvis-Portilla ◽  
Marco Venieri ◽  
Jeffrey A. Priest ◽  
...  

Summary The Duvernay Formation is an unconventional reservoir characterized by induced seismicity and fluid migration, with natural fractures likely contributing to both cases. An alpine outcrop of the Perdrix and Flume formations, correlative with the subsurface Duvernay and Waterways formations, was investigated to characterize natural fracture networks. A semiautomated image-segmentation and fracture analysis was applied to orthomosaics generated from a photogrammetric survey to assess small- and large-scale fracture intensity and rock mass heterogeneity. The study also included manual scanlines, fracture windows, and Schmidt hammer measurements. The Perdrix section transitions from brittle fractures to en echelon fractures and shear-damage zones. Multiple scales of fractures were observed, including unconfined, bedbound fractures, and fold-relatedbed-parallel partings (BPPs). Variograms indicate a significant nugget effect along with fracture anisotropy. Schmidt hammer results lack correlation with fracture intensity. The Flume pavements exhibit a regionally extensive perpendicular joint set, tectonically driven fracturing, and multiple fault-damage zones with subvertical fractures dominating. Similar to the Perdrix, variograms show a significant nugget effect, highlighting fracture anisotropy. The results from this study suggest that small-scale fractures are inherently stochastic and that fractures observed at core scale should not be extrapolated to represent large-scale fracture systems; instead, the effects of small-scale fractures are best represented using an effective continuum approach. In contrast, large-scale fractures are more predictable according to structural setting and should be characterized robustly using geological principles. This study is especially applicable for operators and regulators in the Duvernay and similar formations where unconventional reservoir units abut carbonate formations.


Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1805 ◽  
Author(s):  
Anna Scorzini ◽  
Alessio Radice ◽  
Daniela Molinari

Rapid tools for the prediction of the spatial distribution of flood depths within inundated areas are necessary when the implementation of complex hydrodynamic models is not possible due to time constraints or lack of data. For example, similar tools may be extremely useful to obtain first estimates of flood losses in the aftermath of an event, or for large-scale river basin planning. This paper presents RAPIDE, a new GIS-based tool for the estimation of the water depth distribution that relies only on the perimeter of the inundation and a digital terrain model. RAPIDE is based on a spatial interpolation of water levels, starting from the hypothesis that the perimeter of the flooded area is the locus of points having null water depth. The interpolation is improved by (i) the use of auxiliary lines, perpendicular to the river reach, along which additional control points are placed and (ii) the possibility to introduce a mask for filtering interpolation points near critical areas. The reliability of RAPIDE is tested for the 2002 flood in Lodi (northern Italy), by comparing the inundation depth maps obtained by the rapid tool to those from 2D hydraulic modelling. The change of the results, related to the use of either method, affects the quantitative estimation of direct damages very limitedly. The results, therefore, show that RAPIDE can provide accurate flood depth predictions, with errors that are fully compatible with its use for river-basin scale flood risk assessments and civil protection purposes.


2012 ◽  
Vol 16 (6) ◽  
pp. 1709-1723 ◽  
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward

Abstract. An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results.


Ocean Science ◽  
2012 ◽  
Vol 8 (2) ◽  
pp. 143-159 ◽  
Author(s):  
S. Cailleau ◽  
J. Chanut ◽  
J.-M. Lellouche ◽  
B. Levier ◽  
C. Maraldi ◽  
...  

Abstract. The regional ocean operational system remains a key element in downscaling from large scale (global or basin scale) systems to coastal ones. It enables the transition between systems in which the resolution and the resolved physics are quite different. Indeed, coastal applications need a system to predict local high frequency events (inferior to the day) such as storm surges, while deep sea applications need a system to predict large scale lower frequency ocean features. In the framework of the ECOOP project, a regional system for the Iberia-Biscay-Ireland area has been upgraded from an existing V0 version to a V2. This paper focuses on the improvements from the V1 system, for which the physics are close to a large scale basin system, to the V2 for which the physics are more adapted to shelf and coastal issues. Strong developments such as higher regional physics resolution in the NEMO Ocean General Circulation Model for tides, non linear free surface and adapted vertical mixing schemes among others have been implemented in the V2 version. Thus, regional thermal fronts due to tidal mixing now appear in the latest version solution and are quite well positioned. Moreover, simulation of the stratification in shelf areas is also improved in the V2.


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